Here is a complete Markdown document combining work from ER, LN and DG’s combined analysis on Rare’s Fish Forever survey data.
To start, we’ll read in all relevant CSVs
##
## ── Column specification ────────────────────────────────────────────────────────
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## level2_name = col_character(),
## level3_name = col_logical(),
## level4_name = col_character(),
## ma_name = col_character(),
## ma_status = col_character(),
## reserve_status = col_character(),
## username = col_character(),
## `1_interviewer` = col_character(),
## `2_affiliation` = col_character(),
## `3_community` = col_character(),
## `5_hh_status` = col_character(),
## `6_gender` = col_character(),
## `8_religion` = col_character(),
## `8_religion_other` = col_character(),
## `9_region_name` = col_character(),
## `11k_other_source` = col_character(),
## `14_responsibilities_other` = col_character()
## # ... with 56 more columns
## )
## ℹ Use `spec()` for the full column specifications.
## Warning: 24212 parsing failures.
## row col expected actual file
## 1023 level3_name 1/0/T/F/TRUE/FALSE Maginti '/Users/dylan/Desktop/Summer W: Rare and Tasha/FishForever_Research/new_portal_data/hh_surveys_all.csv'
## 1024 level3_name 1/0/T/F/TRUE/FALSE Kabaena Barat '/Users/dylan/Desktop/Summer W: Rare and Tasha/FishForever_Research/new_portal_data/hh_surveys_all.csv'
## 1025 level3_name 1/0/T/F/TRUE/FALSE Tiworo Utara '/Users/dylan/Desktop/Summer W: Rare and Tasha/FishForever_Research/new_portal_data/hh_surveys_all.csv'
## 1025 35f_ma_gear_mosquito 1/0/T/F/TRUE/FALSE Unknown '/Users/dylan/Desktop/Summer W: Rare and Tasha/FishForever_Research/new_portal_data/hh_surveys_all.csv'
## 1025 35g_ma_gear_poison 1/0/T/F/TRUE/FALSE Not permitted '/Users/dylan/Desktop/Summer W: Rare and Tasha/FishForever_Research/new_portal_data/hh_surveys_all.csv'
## .... .................... .................. ............. .......................................................................................................
## See problems(...) for more details.
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## submissionid = col_character(),
## ma_id = col_double(),
## `44_meeting_attendance` = col_character()
## )
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## submissionid = col_character(),
## ma_id = col_double(),
## `45_leadership_position` = col_character()
## )
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## submissionid = col_character(),
## ma_id = col_double(),
## `48_enforcement_participation` = col_character()
## )
Next, we go through questions 44, 45, and 48 to remove any duplicates
We do this because, when reading in the original CSVs, we saw answers like, “Yes, Yes, Yes,” “No, No, No, No, No” and “Yes male, yes Female, Unsure.”
These questions ask respondents about their household, so it’s possible that they answered individually for each member of their household. However, the goal of this analysis is just to see if any single member of a household participated the management meetings, or enforcement.
The chunks below use recode_factor() to change cells within Qs 44, 45, and 48 to each have a single answer.
## `summarise()` has grouped output by 'submissionid'. You can override using the `.groups` argument.
The following chunk combines duplicated from Question 45, and renames the observations that have been repeated. End result: 1 observation for each submission ID
Note: After Larissa did all the cleaning below, there was an issue witht the recode() function. Dylan replaced each of these with recode_factor() and all factor levels are fine.
## `summarise()` has grouped output by 'submissionid'. You can override using the `.groups` argument.
Below, the same happens for question 48
## `summarise()` has grouped output by 'submissionid'. You can override using the `.groups` argument.
Again, recode() has been switched to recode_factor() for question 48
After recoding/cleaning, these different df were all combined into hhs_complete_all using right_join
## Warning: Missing column names filled in: 'X1' [1]
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## submissionid = col_character(),
## `44_meeting_attendance` = col_character(),
## `45_leadership_position` = col_character(),
## `48_enforcement_participation` = col_character(),
## country = col_character(),
## level1_name = col_character(),
## level2_name = col_character(),
## level3_name = col_logical(),
## level4_name = col_character(),
## ma_name = col_character(),
## ma_status = col_character(),
## reserve_status = col_character(),
## username = col_character(),
## `1_interviewer` = col_character(),
## `2_affiliation` = col_character(),
## `3_community` = col_character(),
## `5_hh_status` = col_character(),
## `6_gender` = col_character(),
## `8_religion` = col_character(),
## `8_religion_other` = col_character()
## # ... with 59 more columns
## )
## ℹ Use `spec()` for the full column specifications.
Here is a quick summary table of the total number of responses, which matches the original number of surveys completed 17712
Alright!
This marks the end of the “cleaning” data section. The final two dataframes (hhs_all_complete and summary) have each survey response as 1 row, with no duplicates, and questions that were answered multiple times (44, 45, and 48) now have 1 answer of Yes, No, or Unsure.
Goal: A robust df with only observations (survey respondents) who have answered every engagement and agreement question.
This will be called hhs_complete_filtered
First, let me get counts of the NAs that we are changing to neutral for Q 44 and 45. We think these two will filter out a large amount of observations:
We see 10,000 NA observations. This is ok, since many of those observations contain NAs for other varables of interest. I.e: they’d be weeded out eventually.
Here is the creation of our filtered df, where each observation responds to every agreement and engagement question. These questions exist in rows 24:33, therefore drop_na is used for those columns.
Update on Agreement From 9/9/2021: In analysis with Dr. Quintana, after looking at variable independence and reframing of what Agreement includes, we are removing the following three questions from the Index: - Question 10 (x10_mpa_important) due to wording that doesn’t focus on MPAs - Question 47 (x47_represent_contributions) due to its near exact math to question 46. This would give these two similar questions as much weight as the other agreement components, which we don’t want. - Question 62 (x62_reserve_compliance) because this question outright asks if fishers follow the rules. This does not have to do with their Agreement - Question 64 (x64_wrong_fishing_reserve) was not asked in Brazil, and we want to include these 756 observations, as the country is a lage focus of the Fish Forever process.
Now columns [24:33] within hhs_complete_filtered are all of the Engagement and Agreement questions.
Dropping NAs: We drop NAs for all Engagement and Agreement Questions.
For Q 46-47 we’ve excluded NAs since they mostly represent “non-fisher households” The difference in n goes from 5640 to ~3300. We want to see how fishers comply though. So the smaller N will show us that.
Let me list out which A and E questions have binary responses, for those that have “neither agree nor disagree,” then I’ll move from -1 to 1, with a neutral option 0.
I’ll start converting the straightforward questions to 1s and 0s. Because there was so much much dang coding in the global_filtered, I’ll make a new df for the numbered version. We’ll call it hhs_filtered_numeric**
Quantification list:
44: Yes/No 1/-1 (We recoded not sure to 0, see Methods)
45: Yes/No 1/-1 (We recoded ’Not sure to 0, see Methods)
48: Yes/No/ Not Sure 1/0/-1
53: Very Often - Never [As a back up, this would be flagged for this being its own index of engagement]
61 (f): Likert Scale (Strongly) Disagree-Agree (1-5)
61 (h): Likert Scale (Strongly) Agree-Disagree
The chunk below uses mutate() and recode to complete the numeration above.
Notes: - For the Likert scale questions that Rare coded from 0-5, I removed the 0s, since they were equivalent to NAs.
All “No management” or “No regulations” ar gone. and all 0s have been removed from questions that rare coded from 0-5. Only 1 question made a significant change, x61f_rights_distribution_fair
Now, I believe it’s time to scale the data!
Let’s just make a quick table with the counts of all the survey responses that we have for analysis.
| Managed Access Area | Number of surveys with all ‘Agreement’ and ‘Engagement’ questions answered |
|---|---|
| SDN_SantaMonica | 256 |
| Antique_Libertad | 238 |
| SDN_SanBenito | 230 |
| SDN_DelCarmen | 227 |
| Antique_Culasi | 222 |
| SDN_GL | 209 |
| Antique_Sebaste | 208 |
| SDN_SanIsidro | 195 |
| SDN_Pilar | 191 |
| RESEX Marinha Mocapajuba | 184 |
| SDN_Burgos | 167 |
| SDN_Socorro | 160 |
| RESEX Soure | 149 |
| SDN_Dapa | 148 |
| RESEX Mãe Grande de Curuça | 143 |
| Santa Fé | 129 |
| Antique_Pandan | 106 |
| RESEX São João da Ponta | 103 |
| RESEX Gurupi-Piriá | 94 |
| Baixo Pinda | 92 |
| Trujillo | 92 |
| Teluk Kolono | 88 |
| Zavora | 84 |
| Pomene | 83 |
| Fequete | 82 |
| Memba | 66 |
| Serissa | 63 |
| Guanaja | 59 |
| Machangulo | 54 |
| NA | 47 |
| Tsondzo | 37 |
| Mucocuene | 34 |
| Wangi-Wangi Selatan | 34 |
| Kitti | 28 |
| Tomia | 24 |
| Farol | 20 |
| Ngiwal | 19 |
| Melekeok | 18 |
| Nhagondzo | 18 |
| Vuca | 18 |
| Petane | 15 |
| Cebu_SanFrancisco | 7 |
| Simuco | 4 |
| Iriona | 2 |
| Puerto Cortés | 1 |
| RESEX Caeté-Taperaçu | 1 |
| Total | 4449 |
Counts by Reserve is great, but Larissa would like the same table by Country
| Country | Number of surveys with all ‘Agreement’ and ‘Engagement’ questions answered |
|---|---|
| PHL | 2564 |
| BRA | 674 |
| MOZ | 670 |
| HND | 330 |
| IDN | 146 |
| PLW | 37 |
| FSM | 28 |
| Total | 4449 |
I’ve double-checked the statistics on each of the above variables! Each new column has a mean of nearly 0 (3x10^-16, etc.)
Since we changed recode() to recode_factor(), these stats functions would not run. By changing the values to as.numeric, all is well.
Let’s start with mean() and sum()
Along with scaling the data onto a Z-Axis, I’m going to simply sum across the agreement and engagement columns too, to see what we get! - eng_mean is the mean index score of engagement, with 1 score for each fisher respondent - agree_mean is the mean score of agreement for each fisher respondent - I add two more variables: “raw_eng_sum” and “raw_agree_sum.” Without scaling, I’ve summed agreement and engagement across rows, for exploratory visualization
These sum variables are exploratory, with the mean score for each observation is the index that we will use.
Here’s a Table of Index scores
| country | Relative Engagement Score | Relative Agreement Score |
|---|---|---|
| PHL | 0.9644028 | -0.7650206 |
| PHL | -0.4500038 | -0.4231813 |
| PHL | 0.1720092 | 0.0118288 |
| BRA | -0.6392887 | 0.0993151 |
| HND | -0.8285736 | -2.1032934 |
| PHL | 0.0517048 | 0.3221292 |
With that, we’ll make a preliminary graph:
## Warning: Removed 49 rows containing missing values (geom_point).
## Warning: Removed 25 rows containing missing values (geom_point).
Here is a graph of Agreement and Engagement measures, colored by responses of “Do you fish inside the reserve?”
Question 63 asks if fishers break the rules, and fish in the reserve. This would be a classic measure of compliance (although self-reporting likely leads to a lower N of admitted non-compliers)
## Warning: Removed 2 rows containing missing values (geom_point).
I want to see how many people said “yes” here:
138 fishers admitted that they fish in the reserve, while 2800 said they do not. The question we’re left with is: What is different about folks who admit to breaking rules? These “Yes” rule-breakers are spread relatively evenly across all countries. Maybe they don’t think there are sanctions, their community doesn’t care about it
Brazil analysis We now include 756 Brazilian observations
Can we (responsibly) mark these folks as neutral? NOPE mena of 64: 3.9/5 (Most fishers list fishing in the reserve as ‘bad’ or ‘very bad’)
We’ll run two different LN/Logit Models: one for agreement, one for engagement
[https://stats.idre.ucla.edu/r/dae/multinomial-logistic-regression/]
Making some tables and graphs for Claudia + Rare:
| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| x44_meeting_attendance | 4449 | 0.053 | 0.969 | -1 | -1 | 1 | 1 |
| x45_leadership_position | 4449 | -0.495 | 0.824 | -1 | -1 | 0 | 1 |
| x48_enforcement_participation | 4449 | -0.533 | 0.774 | -1 | -1 | 0 | 1 |
| x53_encourage_regulations | 4449 | -0.022 | 1.057 | -2 | -1 | 1 | 2 |
| x61g_fishing_change_behavior | 4449 | 3.975 | 0.822 | 1 | 4 | 4 | 5 |
| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| x43_ma_benefits | 4449 | 0.57 | 0.498 | -1 | 0 | 1 | 1 |
| x46_represent_interests | 4449 | 0.649 | 0.594 | -1 | 0 | 1 | 1 |
| x52_ma_benefit_5yrs | 4449 | 0.502 | 0.582 | -1 | 0 | 1 | 1 |
| x61a_current_regulations | 4449 | 3.875 | 0.898 | 1 | 4 | 4 | 5 |
| x61f_rights_distribution_fair | 4449 | 3.77 | 0.92 | 1 | 3 | 4 | 5 |
Great!
First, hhs_scaled is the df that will be used for all analysis.
I’ve run unique() for all the E & A questions, ensuring that we only have responses that are measurable.
Following this with a check of the “no management” answers, and how they were coded.
With the statistics, we see the new mean, sd, etc. for all the agreement and engagement question.
Fully Exploratory
Do not sue the following as results
##
## Call:
## lm(formula = eng_mean ~ x50_ma_punishment + x40_reserve_boundaries_aware,
## data = scaled_sum)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.66591 -0.44466 -0.09244 0.38738 1.87105
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.281473 0.028430 -9.901 < 2e-16 ***
## x50_ma_punishmentna -0.170936 0.130346 -1.311 0.18979
## x50_ma_punishmentNo management 0.362935 0.125148 2.900 0.00375 **
## x50_ma_punishmentNo punishment -0.003160 0.040209 -0.079 0.93737
## x50_ma_punishmentSevere 0.280443 0.032277 8.689 < 2e-16 ***
## x50_ma_punishmentStrong 0.112347 0.021542 5.215 1.93e-07 ***
## x50_ma_punishmentWeak 0.113195 0.039381 2.874 0.00407 **
## x40_reserve_boundaries_aware 0.027613 0.002843 9.711 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.593 on 4080 degrees of freedom
## (361 observations deleted due to missingness)
## Multiple R-squared: 0.04593, Adjusted R-squared: 0.0443
## F-statistic: 28.06 on 7 and 4080 DF, p-value: < 2.2e-16
##
## Call:
## lm(formula = eng_mean ~ x19_current_fish_catch + x22_catch_5yrs +
## x23_job_secure, data = scaled_sum)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.69350 -0.41150 -0.09613 0.37513 1.80737
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.001244 0.025151 -0.049 0.9606
## x19_current_fish_catchDeclined slightly -0.057742 0.027124 -2.129 0.0333
## x19_current_fish_catchImproved heavily 0.181116 0.118112 1.533 0.1252
## x19_current_fish_catchImproved slightly 0.062014 0.040817 1.519 0.1288
## x19_current_fish_catchStayed the same -0.196630 0.030854 -6.373 2.05e-10
## x22_catch_5yrsDeclines slightly -0.037434 0.030021 -1.247 0.2125
## x22_catch_5yrsImproves heavily 0.235881 0.095265 2.476 0.0133
## x22_catch_5yrsImproves slightly 0.155299 0.035652 4.356 1.36e-05
## x22_catch_5yrsStays the same -0.023083 0.031156 -0.741 0.4588
## x23_job_secure 0.111263 0.019129 5.816 6.46e-09
##
## (Intercept)
## x19_current_fish_catchDeclined slightly *
## x19_current_fish_catchImproved heavily
## x19_current_fish_catchImproved slightly
## x19_current_fish_catchStayed the same ***
## x22_catch_5yrsDeclines slightly
## x22_catch_5yrsImproves heavily *
## x22_catch_5yrsImproves slightly ***
## x22_catch_5yrsStays the same
## x23_job_secure ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5911 on 4222 degrees of freedom
## (217 observations deleted due to missingness)
## Multiple R-squared: 0.04974, Adjusted R-squared: 0.04772
## F-statistic: 24.56 on 9 and 4222 DF, p-value: < 2.2e-16
Let me see if there are some that could seem ‘insightful’ for a Rare audience to know. Like knowledge of rules! [Questions 38-40]
Those questions are - Is fishing allowed in the reserve area? (Does fisher X know the rule?) - Would you agree with the statement that most fishers in your community are aware of the boundaries of the fisheries management/managed access area? (Do msot fishers know the rules) - Out of ten fishers in your community, how many would you guess know where the reserve boundary is?
##
## Call:
## lm(formula = eng_mean ~ x38_reserve_fishing_allowed + x39_ma_boundaries_aware +
## x40_reserve_boundaries_aware, data = scaled_sum)
##
## Residuals:
## Min 1Q Median 3Q Max
## -1.82012 -0.45885 -0.06591 0.38553 2.01823
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.013277 0.028401 -0.467 0.640179
## x38_reserve_fishing_allowed 0.147494 0.021622 6.821 1.04e-11
## x39_ma_boundaries_awareDisagree -0.271042 0.043462 -6.236 4.95e-10
## x39_ma_boundaries_awareNeither -0.226942 0.024183 -9.385 < 2e-16
## x39_ma_boundaries_awareNo managed access -0.116381 0.589641 -0.197 0.843543
## x39_ma_boundaries_awareNo reserve -0.365750 0.178321 -2.051 0.040325
## x39_ma_boundaries_awareStrongly agree -0.106235 0.027779 -3.824 0.000133
## x39_ma_boundaries_awareStrongly disagree 0.036610 0.065911 0.555 0.578616
## x40_reserve_boundaries_aware 0.012700 0.003262 3.894 0.000100
##
## (Intercept)
## x38_reserve_fishing_allowed ***
## x39_ma_boundaries_awareDisagree ***
## x39_ma_boundaries_awareNeither ***
## x39_ma_boundaries_awareNo managed access
## x39_ma_boundaries_awareNo reserve *
## x39_ma_boundaries_awareStrongly agree ***
## x39_ma_boundaries_awareStrongly disagree
## x40_reserve_boundaries_aware ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.5893 on 3981 degrees of freedom
## (459 observations deleted due to missingness)
## Multiple R-squared: 0.06071, Adjusted R-squared: 0.05883
## F-statistic: 32.17 on 8 and 3981 DF, p-value: < 2.2e-16
I’ll come back to the regression.
Now, let’s look at a cluster analysis!
Within Engagement and Agreement- We want to see if there are common combinations of answers like “4, 5, 5” for a likert, or something like that.
Now, Clustering
I’m going to explore how many clusters we can see, and try and use a distance-based clustering method first. If possible we want to see common combinations of answers.
The following doesn’t show up in clsuter analysis, but I’d be interested to see… Within 5 questions, if there ae 250 fishers hat say “Yes,” “Slightly agree,” and “No,” we can learn from that.
Basically, it’s going to run 30 different ways of evaluating how many clusters it thinks exist, then tell you the breakdown of what they decide (e.g. “8 algorithms think that there should be 4 clusters”).
NbClust::NbClust()
Now I’ll do K-means clustering to include agreement AND engagement questions.
## K-means clustering with 5 clusters of sizes 701, 842, 936, 1350, 620
##
## Cluster means:
## x44_meeting_attendance x45_leadership_position x48_enforcement_participation
## 1 0.4407989 -0.8587732 -0.3409415
## 2 0.8646081 0.8741093 0.3539192
## 3 0.9786325 -0.8942308 -0.8536325
## 4 -0.9733333 -0.8074074 -0.8807407
## 5 -0.6483871 -0.6629032 -0.7129032
## x53_encourage_regulations x61g_fishing_change_behavior x43_ma_benefits
## 1 1.1298146 4.523538 0.6918688
## 2 0.5095012 4.103325 0.5985748
## 3 -0.7136752 3.747863 0.5769231
## 4 -0.2911111 4.050370 0.5296296
## 5 -0.4161290 3.356452 0.4725806
## x46_represent_interests x52_ma_benefit_5yrs x61a_current_regulations
## 1 0.7318117 0.6918688 4.547789
## 2 0.8432304 0.6401425 3.840855
## 3 0.6880342 0.4476496 3.951923
## 4 0.6703704 0.4792593 4.129630
## 5 0.1887097 0.2322581 2.491935
## x61f_rights_distribution_fair
## 1 4.489301
## 2 3.952494
## 3 3.717949
## 4 3.829630
## 5 2.654839
##
## Clustering vector:
## [1] 2 4 1 5 5 4 2 1 2 2 2 1 5 2 5 5 1 2 4 3 5 3 3 2 4 1 1 4 5 3 1 4 2 2 1 4 5
## [38] 5 5 2 4 4 1 4 2 3 4 4 5 2 1 4 3 3 1 1 2 4 4 3 2 3 2 1 2 1 3 4 1 1 5 3 3 4
## [75] 5 2 4 4 2 4 2 1 2 4 4 5 5 5 2 4 2 4 2 1 1 4 4 1 4 3 5 3 3 3 3 2 1 1 5 4 3
## [112] 4 4 3 5 4 4 4 3 4 4 2 3 2 5 3 5 2 4 3 2 1 2 2 1 4 4 3 1 2 4 3 5 4 2 5 3 3
## [149] 4 4 3 3 3 3 2 1 2 1 4 2 5 2 5 1 4 5 1 3 3 5 4 3 1 4 2 3 4 1 5 3 5 5 1 4 2
## [186] 2 5 2 1 3 3 3 5 1 5 5 2 5 3 5 2 2 3 4 1 3 5 3 4 1 4 4 4 2 4 4 3 3 3 1 5 4
## [223] 5 5 4 2 3 5 4 4 2 5 4 5 2 3 2 1 4 3 5 4 3 2 3 4 3 4 4 3 4 4 1 3 4 2 4 4 5
## [260] 1 1 3 1 4 4 1 1 4 2 5 4 1 3 4 4 3 4 4 4 4 3 2 4 1 4 2 1 1 1 2 4 4 3 4 3 3
## [297] 1 1 4 3 4 4 4 4 4 4 4 4 3 2 5 4 3 3 3 4 2 1 4 4 1 4 1 5 2 2 2 1 4 2 1 2 3
## [334] 3 5 2 4 1 4 2 2 2 4 4 4 3 4 5 4 4 1 1 2 4 1 3 1 3 4 5 3 4 4 2 3 2 4 4 1 2
## [371] 3 3 1 3 1 3 1 4 2 2 3 1 1 4 5 3 5 5 2 3 1 4 3 3 5 4 2 5 5 4 4 2 2 5 1 1 3
## [408] 3 4 4 4 4 5 2 5 1 2 4 4 4 5 4 2 5 4 1 2 3 1 2 5 1 2 4 3 5 1 1 5 1 4 4 2 3
## [445] 3 4 3 3 4 4 2 1 4 4 2 3 4 2 4 4 2 1 1 3 4 3 2 2 3 1 1 4 2 1 3 2 3 3 4 5 5
## [482] 3 1 4 3 3 5 1 2 4 4 2 2 3 2 5 4 3 4 4 4 1 5 1 4 5 2 2 3 3 4 2 5 3 2 3 1 4
## [519] 3 4 4 4 1 4 4 3 5 4 5 2 5 4 3 4 4 1 4 2 5 4 5 4 5 5 3 5 3 2 4 2 4 4 4 3 3
## [556] 4 4 3 2 4 4 1 1 2 5 1 2 4 4 2 4 4 1 2 2 4 2 2 2 1 3 1 3 5 2 3 5 5 4 5 4 4
## [593] 5 2 4 5 4 4 3 5 4 2 5 2 5 3 4 2 4 3 5 4 3 1 2 4 2 3 3 4 4 4 4 2 5 2 1 3 4
## [630] 4 2 4 1 3 3 4 4 2 2 4 4 2 2 1 5 4 2 2 4 5 4 4 4 3 5 3 2 1 2 4 3 3 1 5 3 5
## [667] 4 1 1 1 4 4 3 3 2 1 1 3 3 3 1 5 4 4 1 1 4 3 2 5 4 4 3 4 2 5 4 4 2 4 5 4 4
## [704] 5 5 3 2 5 3 1 5 1 4 4 4 4 2 2 4 3 1 4 4 5 1 3 2 1 3 4 5 3 1 3 4 3 5 3 3 2
## [741] 3 3 1 5 1 4 4 2 3 3 3 1 3 1 1 3 2 5 4 1 4 3 1 1 3 4 3 5 4 1 4 3 4 2 2 2 4
## [778] 5 4 1 2 2 1 4 3 5 3 2 3 5 5 3 2 5 3 4 2 5 2 4 3 4 1 4 4 1 3 3 4 5 3 2 1 4
## [815] 5 3 4 2 2 2 4 3 4 4 1 3 3 1 2 3 1 5 3 3 3 3 4 4 2 2 1 5 3 5 1 2 3 3 5 3 1
## [852] 4 2 3 1 3 4 5 3 4 2 4 3 4 4 3 3 3 4 2 4 2 1 4 4 5 3 4 4 1 1 5 4 4 4 5 4 5
## [889] 3 4 2 2 2 3 1 3 3 3 2 4 3 4 5 2 1 2 2 1 4 2 5 3 4 2 3 2 3 4 3 2 4 3 1 5 4
## [926] 2 4 3 5 1 1 2 3 3 3 4 4 1 2 3 2 4 3 1 5 4 4 4 1 1 5 3 2 2 3 4 2 3 4 3 2 3
## [963] 3 3 2 5 5 2 4 4 3 3 3 4 3 3 4 1 5 3 1 3 3 3 2 1 1 1 3 3 4 5 3 4 2 3 4 4 4
## [1000] 3 1 2 5 2 3 4 3 1 5 4 3 2 1 3 1 5 4 4 4 1 3 2 3 3 5 3 2 3 3 2 5 2 4 2 1 4
## [1037] 3 4 3 4 1 4 2 4 1 4 3 4 4 3 4 2 2 2 5 2 3 4 3 3 1 4 2 2 3 2 5 4 2 1 3 2 2
## [1074] 4 4 3 2 1 3 1 2 4 4 3 4 1 1 5 2 1 3 1 5 1 3 4 3 3 4 2 3 2 5 1 5 1 3 4 3 1
## [1111] 5 4 1 5 3 1 1 4 5 5 3 4 1 4 2 1 2 5 5 3 2 3 2 5 2 4 4 5 3 3 5 1 5 3 3 1 4
## [1148] 3 4 3 1 2 5 4 5 3 4 4 4 4 1 1 5 1 3 1 4 3 2 1 5 3 4 4 3 2 2 4 3 1 4 4 3 4
## [1185] 4 2 4 5 4 4 2 4 4 1 4 3 5 3 4 3 4 3 2 1 4 5 2 4 2 3 5 2 3 1 2 4 5 4 2 2 3
## [1222] 3 3 4 4 3 4 4 5 2 4 4 5 4 1 2 3 1 2 3 5 5 3 3 3 4 4 2 5 3 4 4 5 4 5 4 4 1
## [1259] 3 4 5 5 2 2 4 4 1 5 4 4 1 1 1 3 3 5 2 2 4 4 3 2 1 5 5 2 3 4 2 4 4 3 5 4 3
## [1296] 3 5 1 3 4 2 4 4 5 3 2 4 4 2 3 4 5 4 5 2 3 4 3 4 4 2 1 3 4 4 5 4 4 2 4 1 1
## [1333] 3 2 2 1 4 1 1 2 4 5 1 4 3 2 2 5 4 4 3 4 4 2 5 2 3 5 3 5 2 3 4 3 2 5 5 4 5
## [1370] 4 2 4 4 1 3 1 3 4 4 4 4 4 1 1 4 2 4 4 5 1 2 5 3 3 3 4 4 2 2 1 4 2 1 1 3 4
## [1407] 3 4 1 1 4 4 2 5 4 2 4 1 5 2 1 2 4 4 2 1 3 4 2 5 4 1 3 3 2 2 3 3 2 4 4 4 1
## [1444] 1 3 2 5 3 4 3 1 3 4 3 5 1 3 2 4 2 1 1 4 3 3 1 5 2 5 3 4 4 1 3 4 4 4 4 1 2
## [1481] 4 4 2 4 3 3 4 5 1 3 2 3 1 2 3 1 2 3 1 1 2 4 1 4 2 5 4 1 5 5 2 2 3 2 1 1 2
## [1518] 5 4 5 4 1 2 5 5 4 4 4 2 3 2 1 5 4 3 4 5 3 1 5 5 4 4 2 5 5 3 4 4 1 1 3 4 3
## [1555] 4 3 3 3 1 1 4 5 3 2 4 1 5 4 3 4 1 2 2 5 4 4 4 3 1 3 4 4 4 1 1 1 3 1 5 4 2
## [1592] 5 4 2 2 4 2 3 2 1 4 5 4 4 2 2 5 4 2 4 2 4 1 1 1 3 3 4 1 2 4 4 4 3 3 2 5 2
## [1629] 3 5 3 1 4 3 1 2 2 5 3 4 4 4 4 2 4 3 4 5 3 4 3 2 1 3 4 1 3 3 4 4 4 4 4 4 4
## [1666] 4 2 4 3 3 1 3 3 4 2 5 4 3 1 2 4 4 4 1 3 3 1 2 4 5 4 2 4 2 4 3 3 4 4 3 1 2
## [1703] 4 1 5 1 3 1 3 2 4 3 4 4 4 5 5 3 4 5 3 3 2 2 4 1 2 3 2 4 1 4 4 5 5 3 1 5 4
## [1740] 5 3 3 4 4 4 3 3 3 4 4 4 4 5 3 2 4 5 1 2 5 2 3 4 4 4 5 4 4 4 3 5 1 3 5 5 4
## [1777] 3 4 4 4 1 2 4 5 4 1 4 2 3 3 4 4 5 3 4 3 1 4 4 3 1 2 2 2 4 2 2 1 3 1 4 2 4
## [1814] 4 3 5 5 4 4 4 4 1 5 4 1 1 4 2 4 1 5 3 2 4 5 5 5 3 5 1 1 2 1 3 5 2 5 5 2 5
## [1851] 4 1 1 1 4 2 3 3 4 4 2 4 2 4 4 1 3 5 3 3 4 3 4 4 4 2 3 3 4 2 1 2 4 3 4 1 1
## [1888] 3 2 1 4 3 4 1 4 3 2 1 4 4 3 2 3 2 5 4 4 1 1 3 2 4 3 1 1 1 4 5 1 3 1 4 3 1
## [1925] 4 5 2 3 4 2 1 4 5 2 1 4 4 4 5 5 3 3 2 4 1 5 4 1 2 1 3 4 4 3 1 3 4 4 3 3 3
## [1962] 2 4 2 2 3 3 5 1 5 3 4 1 3 4 1 1 2 5 1 1 3 1 1 4 3 4 2 3 5 2 2 2 3 4 2 4 4
## [1999] 1 3 1 2 4 3 3 3 1 4 3 4 4 4 2 2 3 2 1 4 1 5 4 2 3 3 1 4 4 4 5 2 5 4 5 3 4
## [2036] 1 2 4 4 5 1 4 4 2 4 5 4 4 4 1 3 4 5 5 2 2 4 3 3 4 4 4 3 5 5 1 4 2 4 5 1 2
## [2073] 3 3 4 3 5 3 1 4 4 2 5 3 4 4 1 4 4 2 2 4 4 1 4 5 4 2 1 4 3 4 4 3 5 1 3 2 1
## [2110] 4 4 5 3 4 1 3 5 4 1 2 2 4 4 4 5 3 4 2 4 5 5 4 4 1 4 4 3 4 2 1 2 4 4 5 4 4
## [2147] 4 4 4 2 4 1 4 1 5 4 1 5 5 3 4 2 3 1 2 4 1 4 5 1 4 4 1 1 3 1 2 1 3 3 5 4 2
## [2184] 5 4 4 2 3 4 2 3 4 2 2 5 4 4 5 4 4 4 3 1 5 4 2 3 2 2 2 4 4 3 2 3 3 2 5 4 5
## [2221] 2 4 3 3 2 5 4 2 3 1 4 4 1 4 2 4 2 3 2 3 5 4 4 2 4 5 4 3 2 2 4 5 3 2 3 1 3
## [2258] 2 2 3 5 4 3 5 3 4 3 1 4 2 1 4 2 2 3 1 5 3 1 4 1 2 3 4 5 4 4 1 3 3 2 4 2 3
## [2295] 5 4 3 3 4 5 4 4 5 3 3 5 4 2 2 4 4 3 2 1 3 1 1 4 1 4 3 2 2 3 2 5 4 2 4 5 3
## [2332] 4 2 1 1 2 5 2 4 1 4 3 5 2 4 1 4 1 2 2 2 4 4 1 3 3 4 1 2 4 3 5 4 3 5 5 4 4
## [2369] 3 4 2 5 1 2 4 2 3 3 4 5 4 4 4 2 5 1 5 3 1 2 2 2 4 2 1 3 4 3 3 5 4 4 3 4 3
## [2406] 2 4 3 2 2 4 4 3 2 5 2 2 1 2 3 5 3 2 2 1 1 2 2 1 2 1 1 3 5 1 5 5 5 2 1 1 4
## [2443] 3 3 3 2 4 2 4 4 3 4 3 2 4 3 4 1 5 1 3 1 3 5 3 4 1 3 2 4 3 2 4 1 2 2 1 4 4
## [2480] 5 4 3 3 5 1 2 2 3 3 1 2 1 3 3 3 2 1 2 3 1 1 5 4 2 2 3 1 1 4 4 3 5 5 4 1 2
## [2517] 2 4 5 2 1 4 1 4 1 2 3 5 3 1 2 4 4 4 2 2 4 4 5 5 3 5 3 2 4 4 4 3 4 4 3 4 4
## [2554] 5 3 3 3 3 4 5 1 4 4 4 3 1 1 4 3 3 2 1 3 5 3 3 4 3 4 3 1 5 3 3 4 3 4 5 1 4
## [2591] 2 1 3 3 3 3 5 2 2 4 4 4 1 4 4 2 3 4 4 4 4 3 1 3 5 1 2 4 2 4 4 5 3 3 4 4 3
## [2628] 3 2 5 1 1 2 4 3 4 4 3 2 3 5 3 5 2 2 1 4 2 2 5 3 4 2 4 3 3 4 4 3 1 1 3 5 3
## [2665] 3 4 4 2 1 1 1 4 2 1 2 4 4 1 3 1 1 5 5 4 5 2 3 4 4 3 2 4 4 5 4 4 2 5 1 3 2
## [2702] 1 4 3 3 5 2 1 4 4 4 2 4 3 3 5 5 1 5 1 1 5 4 2 4 2 1 5 2 2 1 2 5 1 4 3 3 5
## [2739] 2 4 4 1 3 2 3 2 5 3 2 5 2 3 4 4 1 1 3 4 5 4 4 4 5 5 4 4 1 5 3 2 2 4 4 3 3
## [2776] 5 4 5 2 3 3 1 4 2 3 4 5 4 2 4 4 2 2 4 5 5 1 4 3 2 3 1 4 2 4 4 3 4 3 1 2 3
## [2813] 4 1 5 3 5 2 3 2 5 3 4 4 3 4 4 2 3 2 4 1 3 4 2 1 4 3 2 2 4 5 2 2 4 3 3 2 4
## [2850] 2 3 3 2 4 2 4 5 2 3 2 4 5 5 5 3 3 1 4 4 5 3 3 5 1 4 4 2 4 2 4 3 2 4 3 2 4
## [2887] 1 4 4 5 2 5 4 4 4 4 3 3 2 4 4 2 1 1 5 3 3 2 5 5 5 3 3 1 5 3 5 5 4 4 5 5 4
## [2924] 2 3 2 1 4 2 2 3 4 3 3 1 5 4 4 4 3 3 4 5 3 1 5 4 1 3 4 4 2 3 2 2 1 4 3 2 5
## [2961] 5 1 4 1 5 3 1 1 1 1 1 5 4 2 1 5 3 3 4 2 4 1 5 3 1 2 5 2 4 3 1 4 4 4 5 1 1
## [2998] 1 5 4 3 1 5 5 3 4 1 2 5 3 3 2 5 4 3 5 1 1 1 4 2 2 4 2 2 1 4 3 4 4 3 5 1 2
## [3035] 4 5 2 2 5 5 3 4 5 3 4 4 2 2 4 3 2 4 1 1 5 1 4 2 3 3 4 1 4 5 1 4 4 3 4 5 1
## [3072] 1 1 1 2 2 4 5 4 3 4 2 5 4 4 1 2 3 2 1 1 4 4 2 2 4 4 1 4 3 1 4 3 1 5 4 4 4
## [3109] 4 3 4 4 2 4 4 3 3 5 1 3 5 1 3 1 1 3 3 4 1 3 2 2 4 1 2 5 3 3 4 2 3 5 1 1 3
## [3146] 5 5 3 4 2 5 4 4 2 1 1 1 4 4 4 2 3 2 1 5 2 2 4 4 2 1 2 3 1 4 5 2 3 2 2 3 3
## [3183] 4 3 3 2 4 5 1 2 5 5 2 2 1 5 4 5 3 4 2 3 1 4 4 4 4 2 5 3 4 2 2 2 3 5 4 2 2
## [3220] 4 3 3 4 4 3 5 5 4 3 5 3 4 5 3 4 5 4 1 5 4 1 4 3 3 3 2 1 2 4 4 5 3 4 2 1 4
## [3257] 1 2 1 2 3 4 4 4 4 4 5 1 2 1 2 3 2 5 2 3 3 1 4 4 2 2 5 4 4 3 2 1 2 1 3 3 4
## [3294] 1 4 3 4 3 3 3 3 3 3 3 4 4 4 1 2 5 4 1 1 2 4 4 2 2 4 4 3 4 4 2 5 4 3 1 3 2
## [3331] 4 3 2 4 1 4 2 5 1 4 2 1 5 4 3 2 5 4 1 4 2 4 2 5 2 2 2 4 4 5 3 2 1 1 4 4 4
## [3368] 5 4 3 5 3 3 4 4 4 1 2 1 2 2 2 1 1 4 4 2 4 3 4 4 5 5 1 1 1 3 1 4 5 4 5 2 4
## [3405] 2 2 1 4 5 1 1 1 4 2 4 1 2 4 2 2 3 5 1 5 4 2 4 1 5 3 5 2 2 4 5 5 2 4 3 1 5
## [3442] 2 4 2 3 3 1 2 3 4 3 2 2 1 3 5 4 4 4 3 5 4 5 1 1 4 1 3 3 1 4 4 4 4 3 3 2 4
## [3479] 3 4 2 5 1 1 5 4 2 4 3 3 2 4 4 4 2 2 2 2 4 4 4 1 5 1 2 2 5 4 4 4 5 2 5 3 2
## [3516] 1 2 1 4 3 5 5 3 2 1 1 4 3 4 3 2 3 5 4 1 5 2 4 3 3 4 5 5 4 3 4 1 4 5 2 2 3
## [3553] 4 4 4 1 4 2 2 2 4 1 3 4 2 2 3 2 3 5 4 2 1 4 2 4 3 2 1 2 3 4 5 2 3 3 4 1 5
## [3590] 1 1 4 2 3 4 2 2 1 4 3 4 2 1 4 4 4 4 4 4 4 4 5 1 5 5 4 4 3 1 4 1 3 3 3 4 4
## [3627] 2 4 4 3 5 4 4 5 4 2 3 4 1 2 2 5 2 3 1 4 2 2 3 4 4 4 3 5 1 2 4 4 2 4 4 4 2
## [3664] 4 2 4 2 2 4 3 5 4 5 3 1 5 2 2 1 5 5 1 2 1 5 2 4 3 1 4 4 5 3 4 2 4 4 4 4 3
## [3701] 1 5 3 4 1 4 5 1 2 3 4 4 4 3 4 2 3 4 2 2 5 4 2 4 2 5 3 1 3 3 2 3 5 1 4 1 1
## [3738] 3 2 3 4 3 2 1 3 2 2 3 2 4 1 4 1 2 3 5 5 4 4 5 4 1 4 2 4 3 4 3 2 4 2 2 1 5
## [3775] 2 2 3 2 3 2 2 3 3 4 3 2 4 2 5 2 1 2 3 1 3 4 4 4 4 5 5 2 4 2 1 3 4 2 2 1 4
## [3812] 1 2 5 2 2 4 5 3 5 1 3 4 2 1 1 3 4 4 1 5 4 1 1 1 3 2 1 3 2 4 4 2 3 5 4 4 4
## [3849] 1 3 4 3 2 1 2 2 2 5 2 2 4 1 2 2 1 4 4 5 1 4 2 3 4 3 2 4 3 4 5 1 3 5 4 5 4
## [3886] 4 2 2 4 4 2 1 2 5 5 4 2 1 4 4 4 5 4 4 4 1 5 1 1 4 5 1 1 2 4 4 1 4 4 4 4 4
## [3923] 5 5 4 4 2 1 4 3 1 2 5 5 4 5 5 2 4 5 2 3 4 1 4 4 2 1 4 1 1 4 2 2 1 5 4 3 1
## [3960] 4 5 4 2 3 2 3 3 1 1 2 3 1 5 3 3 4 5 2 1 5 1 5 4 4 3 3 1 4 4 4 5 5 2 3 4 3
## [3997] 4 4 2 5 4 4 4 4 4 2 3 4 2 5 1 4 2 3 4 5 3 4 4 4 2 4 3 2 2 5 1 5 4 2 4 2 4
## [4034] 1 2 4 4 3 2 3 2 4 3 5 5 2 5 2 3 2 2 3 4 4 4 4 4 2 1 4 3 3 5 5 5 4 3 3 5 4
## [4071] 5 2 4 4 2 3 4 2 3 2 4 4 2 1 5 1 4 1 2 4 2 3 2 4 4 4 4 3 1 4 3 5 4 5 3 4 4
## [4108] 4 3 3 4 3 4 2 5 2 5 5 1 5 3 3 3 4 3 3 3 2 1 3 4 4 4 2 4 1 3 3 1 5 3 4 4 1
## [4145] 3 4 4 2 4 5 4 1 5 3 3 4 1 3 5 4 5 3 4 2 5 4 3 4 1 3 3 3 3 4 2 4 1 2 5 4 1
## [4182] 5 3 4 1 4 5 2 4 4 4 4 3 5 2 2 3 2 2 2 2 3 5 5 2 4 4 4 5 4 4 3 3 2 2 3 1 3
## [4219] 5 3 3 4 2 4 1 4 5 4 4 5 1 5 3 4 1 3 1 5 4 3 4 4 3 3 4 3 4 5 3 3 4 5 3 4 3
## [4256] 2 3 4 4 2 2 5 5 2 4 1 1 5 5 3 1 1 4 3 5 5 4 3 3 5 5 1 4 2 1 1 4 3 1 5 3 3
## [4293] 2 4 1 3 5 4 5 3 2 5 1 1 3 5 2 4 3 2 1 3 4 4 4 4 2 3 4 1 1 1 4 4 3 1 1 4 4
## [4330] 4 4 3 4 4 3 2 4 1 3 2 1 3 4 2 2 4 4 3 5 5 4 4 4 1 3 4 4 5 1 4 2 3 4 5 5 3
## [4367] 3 3 4 1 2 1 5 3 2 3 1 1 1 4 2 2 2 4 5 1 1 1 4 3 4 4 1 4 4 3 4 1 2 1 3 3 2
## [4404] 1 2 4 2 3 3 2 2 2 2 2 3 5 5 3 1 3 5 4 2 5 4 1 1 4 2 5 2 1 3 4 4 5 3 1 3 2
## [4441] 3 3 3 2 4 3 3 1 4
##
## Within cluster sum of squares by cluster:
## [1] 2910.662 3338.550 3249.987 4476.612 3886.760
## (between_SS / total_SS = 39.2 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"
## agreement_engagement_km.size x44_meeting_attendance x45_leadership_position
## 1 701 0.4407989 -0.8587732
## 2 842 0.8646081 0.8741093
## 3 936 0.9786325 -0.8942308
## 4 1350 -0.9733333 -0.8074074
## 5 620 -0.6483871 -0.6629032
## x48_enforcement_participation x53_encourage_regulations
## 1 -0.3409415 1.1298146
## 2 0.3539192 0.5095012
## 3 -0.8536325 -0.7136752
## 4 -0.8807407 -0.2911111
## 5 -0.7129032 -0.4161290
## x61g_fishing_change_behavior x43_ma_benefits x46_represent_interests
## 1 4.523538 0.6918688 0.7318117
## 2 4.103325 0.5985748 0.8432304
## 3 3.747863 0.5769231 0.6880342
## 4 4.050370 0.5296296 0.6703704
## 5 3.356452 0.4725806 0.1887097
## x52_ma_benefit_5yrs x61a_current_regulations x61f_rights_distribution_fair
## 1 0.6918688 4.547789 4.489301
## 2 0.6401425 3.840855 3.952494
## 3 0.4476496 3.951923 3.717949
## 4 0.4792593 4.129630 3.829630
## 5 0.2322581 2.491935 2.654839
| x44_meeting_attendance | x45_leadership_position | x48_enforcement_participation | x53_encourage_regulations | x61g_fishing_change_behavior | x43_ma_benefits | x46_represent_interests | x52_ma_benefit_5yrs | x61a_current_regulations | x61f_rights_distribution_fair |
|---|---|---|---|---|---|---|---|---|---|
| 0.4407989 | -0.8587732 | -0.3409415 | 1.1298146 | 4.523538 | 0.6918688 | 0.7318117 | 0.6918688 | 4.547789 | 4.489301 |
| 0.8646081 | 0.8741093 | 0.3539192 | 0.5095012 | 4.103325 | 0.5985748 | 0.8432304 | 0.6401425 | 3.840855 | 3.952494 |
| 0.9786325 | -0.8942308 | -0.8536325 | -0.7136752 | 3.747863 | 0.5769231 | 0.6880342 | 0.4476496 | 3.951923 | 3.717949 |
| -0.9733333 | -0.8074074 | -0.8807407 | -0.2911111 | 4.050370 | 0.5296296 | 0.6703704 | 0.4792593 | 4.129630 | 3.829630 |
| -0.6483871 | -0.6629032 | -0.7129032 | -0.4161290 | 3.356452 | 0.4725806 | 0.1887097 | 0.2322581 | 2.491935 | 2.654839 |
There are 5 clusters!
Sizes: 701 842 936 1350 620
1 and 2 seem similar, with high levels of A and E - 44: 0.44 (C1) and 0.86 (C2) Cluster 2 attends more meetings - 45 is a big swing, cluster 2 is 0.87, and C1 is -0.85. These are the leaders! - 48: -0.44 and 0.35 is a decent difference in enforcement BUT C1 is more likely to encourage regulations (1.21 v 0.5) -61 is closer (4.5 and 4.1)
Within agreement they’re more similar. but C1 is higher average in each of the 5 questions. Less leaders, but agree more across the board.
3 and 4 - 44: C3 has a 0.97, and C4 has a -0.97 45 is similar (within .01) Same for 48 53 (encourage regulations) is -0.7 (C3) and -0.29 (C4) 61g change behavior C3 is the 2nd lowest at 3.7, and c4 is in third at 4.05 Within agreement, they’re averages are similar across all questions, with C3 a little bit lower for most, except ma_benefits
More than anything, C3 ATTENDS a whole lot more than C4. but C3 is the least likely of any group to encourage regulations. Perhaps we’re seeing creative fishers here
C5 is looking at 2/5 for questions 61a and 61f, and is also by far the lowest for 46 and 52. within agreement, only in MA benefits are they somewhat close to other clusters. They do have a few more leaders than other clusters though. But overall, very much not attending
## Warning: Removed 10 rows containing missing values (geom_point).
Clusters with question 64: How wrong is fishing in the reserve?
## Warning: Removed 1336 rows containing missing values (geom_point).
Table version:
| cluster_no | x64_wrong_fishing_reserve | n |
|---|---|---|
| 1 | 5 | 272 |
| 1 | 4 | 197 |
| 1 | 3 | 65 |
| 1 | 2 | 6 |
| 1 | 1 | 3 |
| 1 | NA | 158 |
So, kind of everyone thinks its pretty wrong, huh. Also a ton of NAs, hard to limit that noise
## Warning: Removed 3 rows containing missing values (geom_point).
Right away this is really interesting!
Cluster 1 Only 1 observation is below -1 in agreement. Most of the fully-agreeing fishers are in cluster two - but not all! It excludes the agreeing, but rather unengaged fishers. Similarly, it omits the super-engaged. We can describe C1 (so far) as fishers who are very on board with rules, but are a bit above average engagement. Centers = (0.448207772, 0.3930240) (Agreement, Engagement) N = 701
Cluster 2 These are all the super-engaged fishers. Agreement ranges from 1 (the maximum) to -1.4. Center = (0.156193496, 0.8606600) (Agreement, Engagement) N = 842
Cluster 3 This is the largest spread of any cluster. We have some fully-agreeing fishers, and at least 1 or 2 with agreement below 1.5. Engagement grows up to 1.5, which is mostly reserved for the high-engaging clusters. With means SO close to 0, we see this as a the most average cluster. A bit less engaged, and agreeing with as much as the average fisher from the study. Center = (0.002805289, -0.1748106) (Agreement, Engagement) N = 936
Cluster 4 Its center is very close to Cluster 3, but we can see clear differences when we look at the spread. These folks do NOT engage. While there as unengaged as the Resistant fishers, but most actually agree quite a lot. This includes some with A = 1, the max. Center = (0.052635489, -0.4098127) (Agreement, Engagement) N = 1350, largest cluster
Cluster 5 - Resistant This is the classically known group of unengaged, disagreeing fishers. Many of the outliers are from Honduras, but the country variable doesn’t explain the entire cluster. They all disagree, but to varying levels. We’ll look at their distinguishing characteristics. Center = (-0.837729407, -0.4569608) (Agreement, Engagement) N = 620, smallest cluster.
Notes: - Resistant fishers are far more present in BRA, HND and PHL. However, HND and BRA both spread from the lowest to highest engagement possibilities. This mean there is OPPORTUNITY to be a leader, enforce, etc.
Plotting clusters by gender Female mean = (-0.06947793, -0.03637067)
Male mean = (0.04032597, 0.02179388)
This was not the case before! Were all within 0.1 of zero, to be clear. But when we add Brazil, the mean for men increases
7 machines said there are two clusters, I’m going to run this as well and compare a little:
To do so, I’ll make 4 df, one for each cluster.
## [1] 0.393024
## [1] 0.4482078
## [1] 0.3411713
## [1] 0.5396342
## [1] 0.86066
## [1] 0.1561935
## [1] 0.9495807
## [1] 0.1382421
## [1] -0.1748106
## [1] 0.002805289
## [1] -0.2266834
## [1] 0.04425969
## [1] -0.4098127
## [1] 0.05263549
## [1] -0.4500038
## [1] 0.1214331
We see some slight changes between means and medians, BUT we care about the outliars, (mostly the very low engagement and agreement.) So, we’ll use the mean
Cluster 1 Notes:
A subset of quite agreeing, and somewhat engaged fishers.
Cluster 2 Counts: BRA 25, HND 29, IDN 33, MOZ 322, PHL 422
Notes: Committed fishers, easily the highest engaging group. Like C1, has a good chunk of high-agreeing fishers also. Those top engagement scores seem to come from nearly all countries (less from Honduras.)
Cluster 3 Notes:
Just as with the full graph, this has the most spread, centered very close to (0,0.) on Engagement we see -1 up to 0.5 and -0.5 to 1 with agreement.
BRA 67, FSM 1, HND 34, IDN 18, MOZ 25, PHL 783, PLW 8
Cluster 4
## Warning: Removed 1 rows containing missing values (geom_point).
otes: We see that this is determined by low engagement, but decent to high agreement. The cluster ranges from -1 to 1 on agreement. These folks are OK with rules for the most part. However, they don’t take part in the management process. More than ⅓ of all Brazil’s respondents are in this cluster.
BRA 283, FSM 6, HND 126, IDN 32, MOZ 167, PHL 731, PLW 5
Cluster 5
## Warning: Removed 9 rows containing missing values (geom_point).
Resistant Fishers
With a mean at (-0.5, -.8) this is a disagreeing, unengaged group, including some crazy disagreeing outliers, from Honduras. There are a chunk of observations above 0 in engagement, but only 1 on the agreement axis. N = 620, smallest cluster.
Philippines: We see this breakdown:
Indonesia:
## Warning: Removed 1 rows containing missing values (geom_point).
Counts C1: 10 (7%) C2: 33 (23%) C3: 18 (12%) C4: 32 (22%) C5: 53 (36%) Honduras
## Warning: Removed 1 rows containing missing values (geom_point).
Counts:
C1: 6 (21%) C3: 1 (4%) C4: 6 (21%) C5: 15 (54%)
Mozambique
## Warning: Removed 7 rows containing missing values (geom_point).
This is not part of our continuing analysis
So R has suggested that there are 3 meaningful clusters for engagement.
For now, I run the clusters, and we can go back.
Looking at 3 clusters:
OK! That’s a whole lot of information.
I’ll look at a couple key factors
Size of clusters: [1] 1277 878 959
Now that these clusters are attached to each observation, let’s visualize it
This is great, let’s map them over country and reserve as well.
corrplot() When running a correlation of all the Engagement questions, we see very little correlation across the different questions. We see correlations around 0 and 0.3, with no negative correlations. We find the largest correlation between questions 45 and 48 (0.5).
Let’s do the same with agreement, and all compliance questions together.
Here we see really similar results to the Engagement corrplot. There are no dark blue or large circles. Again, we see no negative correlation, a lot of results between 0 and 0.2. 61(f) (fairness of distribution) and 61(a) (benefit of current regulations) have a slightly lager correlation, at 0.5
Let’s look at agreement AND engagement. This will look messy at first.
Here, we see nothing new, which is interesting! There are no strong correlations across Engagement and Agreement. This suggests that they are genuinely different measurements of compliance. Also of note, there are ZERO negative correlations between an agreement question and an engagement question. I thought that we might see something below zero, but all of these questions track towards compliance. Quite useful stuff.
Engagement
Question 44: Mean = 0.138, but nearly all respondents said Yes or No. Around 1200 responses did not attend meetings, will around 1600 do attend.
Question 45
Question 45: Mean = -0.551. This mean shows that 3/4 of fishers responded “No” Few fisher households included someone with a leadership position within the fishery.
Question 48
Question 48: Mean = -0.492. Nearly 3 out of 4 fishers do not participate in enforcement.
A quick pause from graphs!
Let’s see the combination counts for question 45 and 48
Question 53: Mean = -0.071. This is just below the “neutral” survey option. We see a very similar totals in the “strongly agree” and “strongly disagree.” Our mean skews negative because around 100 more fishers wrote “disagree” relative to “agree.”
Question 61(g) mean = 3.955. This is the first mean that really skews high. With a mode of 4, most respondents were at least “willing” to change their fishing behavior for the reserve structure.
Here’s a check for normal distrbution: It looks pretty normal, even with the high amount of “4” responses
Moving onto Agreement
43: Mean = 0.556. Nearly no one answered with the negative response.
*Question 46: Mean = 0.667. Huge skew towards the positive response.
We saw that variables 46 and 47 are highly correlated, and have similar wording. We find them to NOT be independent from one another, and exclude 47 from the index of Agreement.
*Question 52: Mean = 0.506. For the first time, we see a lot of “unsure” answers. This likely has to do with uncertainty in projecting 5 years in the future.
Question 61(a): Mean = 3.93, clear positive skew, and the mean seems to represent the mode and median
61(f): Mean = 3.805. More neutral responses that strongly agree, but very few disagreeing fishers.
Question 62: Mean = 0.525
We do NOT include this question in the index, but will use it to create a graph of “admitting noncompliance”
Question 64: Mean = 3.957. Another mean between 3 and 4, with very small numbers of below-neutral responses
Starting with Enforcement
“Who is responsible for enforcement in your community? 1. Fisheries Management Body 2. National Government 3. Subnational Government 4. Other (please state) 5. There is no enforcement system”
What do we see??
Dyl hypothesis: National government would show us less engaged fishers
Let’s get some count data (classic post-visualization move)
| cluster_no | Fisheries Management Body | Myself | National Government | No enforcement system | Other | Subnational Government |
|---|---|---|---|---|---|---|
| 1 | 251 | 45 | 199 | 14 | 6 | 166 |
| 2 | 603 | 16 | 123 | 4 | 16 | 60 |
| 3 | 481 | 14 | 125 | 14 | 8 | 270 |
| 4 | 511 | 28 | 248 | 67 | 12 | 445 |
| 5 | 248 | 14 | 123 | 89 | 14 | 88 |
| Total | 2094 | 117 | 818 | 188 | 56 | 1029 |
Means of Agreement and engagement, based off enforcement type
Comparing “Subnational government” and “No enforcement system”
## Warning: Removed 10 rows containing missing values (geom_point).
| Variable | N | Mean | Std. Dev. | Min | Pctl. 25 | Pctl. 75 | Max |
|---|---|---|---|---|---|---|---|
| eng_mean | 188 | -0.485 | 0.5 | -1.559 | -0.829 | -0.207 | 1.586 |
| agree_mean | 188 | -0.498 | 0.73 | -2.207 | -0.983 | 0.101 | 0.98 |
| cluster_no | 188 | ||||||
| … 1 | 14 | 7.4% | |||||
| … 2 | 4 | 2.1% | |||||
| … 3 | 14 | 7.4% | |||||
| … 4 | 67 | 35.6% | |||||
| … 5 | 89 | 47.3% | |||||
| x49_enforcement_responsible | 188 | ||||||
| … No enforcement system | 188 | 100% | |||||
| x51b_fishers_reserves | 184 | 2.81 | 3.613 | 0 | 0 | 5 | 10 |
| country | 188 | ||||||
| … BRA | 142 | 75.5% | |||||
| … HND | 43 | 22.9% | |||||
| … MOZ | 3 | 1.6% |
No enforcement system lacks engaged folks, perhaps because they cannot volunteer for enforcement There is more high agreement among fishers who believe that their National Government is responsible for fishery enforcement The very-unengaged (below -1) have an even spread across the 3 types. Among the No enforcement: Variable N Mean Std. Dev. Min Pctl. 25 Pctl. 75 Max eng_mean 188 -0.485 0.5 -1.559 -0.829 -0.207 1.586 agree_mean188 -0.498 0.73 - 2.207 -0.983 0.101 0.98 47% in C5, and 35% in C4. 75% from Brazil, 22% Honduras. There are a few outliars who engage heavily, and at least 1 who completely agrees. However, these folks are mostly resistant. 1 SD above the mean in right around (0,0)
Color-blind Dyl here, uh oh! I’m going to need to see some tables FMP has fewer fishers who heavily disagree. That said, I can’t see much from these graphs so far, other than the interpretations from the bar graph.
OK! Dad wants to see boat ownership Hypothesis: Fishers without boats are less likely to engage
Visual Interpretation
Let’s break this down by country, to check if boat status and country are heavily correlated
A lot of renting on Honduras! Mozambique and Brazil have a lot of collective ownership Brazil also has a lot of boat owners Mozambique has the highest percentage of fishers who fish without boats, even more than Brazil (where I know many fishers collect crabs and other invertebrates)
Hypothesis: Boat employees fish industrially
OK, too hard to know. 231 NA, 15 not NA. No correlation
Visualizing Need + Food Security
HHS questions that relate to Need
Starting with 55 and 56
This is going to be a bunch of visuals, and some comparison to see what’s actually shown in the data.
Dyl’s hypotheses: - Fishers with limited funds are less engaged, but vary evenly across agreement - Fishers who eat lots of seafood are more engaged than those who don’t - Cluster 1 (agreers) make more money than Cluster 2 (super-engaged)
## Warning: Removed 10 rows containing missing values (geom_point).
Cluster 1: 66% job secure Cluster 2: 72% job secure Cluster 3: 55% job secure Cluster 5: 46% job secure
Question 29 “To cover family needs your household income is…” [Sufficient, Tight, Insufficient]
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Let’s look at some count numbers (percentages are numbers!)
C1: 27% Insufficient, 15% sufficient, 57% Tight 671 total C2: 25% Insufficient, 21% Sufficient, 54% Tight 780 total C3: 26% Insufficient, 10% Sufficient, 63% Tight 887 total C4: 11% Insufficient, 17% Sufficient, 70% Tight 1241 total C5: 21% Insufficient, 21% Sufficient, 58% tight 584 total
Since need (so far) isn’t correlating much with any particular level of compliance, let’s see how people who are dependent on seafood comply:
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Even looking at counts, we’re seeing another variable without any correlation - this si kinda crazy!
What if - nothing really drives compliance behaviors other than simply attitudes about compliance?
… There are plenty more variables to explore before we truly wonder that, though.
Let’s look at x55_worry_food
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Alright, now we see some spread.
Those who “often” worry about food are MORE engaged and also the most resistant.
This is challenging because there is a far larger N within “sometimes,” but we can compare the differences of “Never” and “Often.” Never has some very disagreeing fishers, who engage a little (willing to change behavior, likely)
Those who often worry about food have a lot of somewhat engaged, rather agreeing folks. Let’s do some stats here. He
## Warning: Unreplaced values treated as NA as .x is not compatible. Please specify
## replacements exhaustively or supply .default
## Warning in x50_ma_punishment != c("No management", "NA", "na"): longer object
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## Warning in cor(first_correlate_df, use = "pairwise.complete.obs", method =
## "spearman"): the standard deviation is zero
## Warning in cor(first_correlate_df, use = "pairwise.complete.obs", method =
## "spearman"): the standard deviation is zero
## Warning in cor(first_correlate_df, use = "pairwise.complete.obs", method =
## "spearman"): the standard deviation is zero
## Warning in cor(first_correlate_df, use = "pairwise.complete.obs"): the standard
## deviation is zero
Correlation with Engagement/Agreement:(N = Negative, P = Positive, options of strength = c(week, moderate, strong)) [E/A] - x11e (P, weak) [E]
Other interesting correlations - x64 (wrong to fish in the reserve) and x11h (income from extraction) (P, moderate) - x61d (strong enforcement) and x11h (income form extraction) (N, moderate) - Strong negative correlation between 11i and 11h (tourism and extraction incomes) - x64 has negative correlations with x61c and x61c (community participation and strong enforcement) - x61c through x61 i have moderate positive correlations
Making a table of all these variables, by cluster
Let’s compare question 19 across the axes and the clusters
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## Warning: Removed 10 rows containing missing values (geom_point).
Eye-test notes: - Declined a lot has most of the resistant outliars, has less average fishers AND less of the high-agreement, low-engagement folks - Improved Slightly: not too many in full-agreement with rules, but very even across the engagement axis. Also miss thing high-agreement, low-engagement fishers. - Stayed the same has a limited amount of high-engaging fishers, we see a lot of high-agreement.
For me, I’d like to see some counts
Here’s a breakdown of the ecology question:
Declined a lot: 871
Declined slightly: 1855
Improved heavily: 27
Improved slightly: 372
Stayed the same: 1169
Cluster 1 saw mostly declines (466/677) Cluster 2 has the most who saw slight improvements Cluster 3
I just want to check the counts of x19 by country